Triple
T9817636
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rovereto |
E238447
|
entity |
| Predicate | hasMuseum |
P105
|
FINISHED |
| Object | MART |
E823446
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: MART | Statement: [Rovereto, hasMuseum, MART]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MART Context triple: [Rovereto, hasMuseum, MART]
-
A.
MART
chosen
MART is a prominent modern and contemporary art museum in Rovereto, Italy, renowned for its extensive collections and striking contemporary architecture.
-
B.
Mart.
Mart. is the standard botanical author abbreviation for the German botanist and explorer Carl Friedrich Philipp von Martius, known for his extensive work on Brazilian flora.
-
C.
Martz
Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
-
D.
MAUR
MAUR is the Management Authority for Urban Railways, a government body responsible for overseeing the development and operation of urban rail transit systems.
-
E.
MAR
MAR is the three-letter ISO 3166-1 alpha-3 country code assigned to Morocco.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca84dfde1481909f47c286d715f892 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb2f5bfa481908a2d2cb3f3d7d585 |
completed | April 2, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e41725948190a40ddcf9552e9bf1 |
completed | April 5, 2026, 4:24 a.m. |
Created at: March 30, 2026, 8:30 p.m.